Triple
T22597298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Yellow Admiral |
E574714
|
entity |
| Predicate | hasOccupationOfDeuteragonist |
P148874
|
FINISHED |
| Object | physician |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: physician | Statement: [The Yellow Admiral, hasOccupationOfDeuteragonist, physician]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOccupationOfDeuteragonist Context triple: [The Yellow Admiral, hasOccupationOfDeuteragonist, physician]
-
A.
hasCoProtagonistOccupation
Indicates that two or more co-protagonists share a specified occupation or professional role.
-
B.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
-
C.
antagonistOccupation
Indicates the role, job, or professional activity that the antagonist character performs.
-
D.
laterMainCharacterOf
Indicates that one entity becomes the main character of a work at a later point in time, succeeding another main character.
-
E.
hasOccupationOfDesignee
Indicates that one entity serves as the designated or appointed holder of an occupation or role for another entity.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e245bc11308190b69d794d5d1e0bb6 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f16269a56881909bb5af0258150f93 |
completed | April 29, 2026, 1:44 a.m. |
| PD | Predicate disambiguation | batch_69ee627be4248190889a88764624e174 |
completed | April 26, 2026, 7:07 p.m. |
| PDg | Predicate description generation | batch_69ee8841e9cc81908d23b34215e3be71 |
completed | April 26, 2026, 9:48 p.m. |
Created at: April 17, 2026, 2:50 p.m.